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Genkit Tool Calling: Give AI Models (LLMs) the Tools to Get Things Done
- AI Models need tools like Retrieval-Augmented Generation (RAG) to tackle specific tasks beyond their training data limitations.
- Function calling in Genkit enables AI Models to interact with systems and external data sources through structured methods.
- Genkit's tool creation involves defining tools with descriptive names, descriptions, and implementation functions.
- Using tools like GetCustomerDetails in AI Models to fetch specific customer data enhances response accuracy.
- Genkit offers a developer UI for testing flows, tools, and interactions in a user-friendly manner.
- Defining flows in Genkit involves setting up input-output schemas and executing functions for specific tasks.
- Adding tools like GetCustomerOrders and GetProductDetails broadens an AI Model's capabilities for diverse queries.
- Testing flows and tools in the Genkit Developer UI helps ensure proper functionality and troubleshooting.
- Function calling in Genkit allows for tailored AI responses to user queries in various contexts.
- Genkit empowers users to bridge the gap between AI hype and practical, real-world applications with AI agents.
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